Assessment of Missing Data on Glaucoma Severity among Participants in the NIH All of Us Research Program of the United States.

PRECIS: There were statistically significant differences across multiple socioeconomic characteristics and self-reported barriers to care among primary glaucoma patients with severity staging data versus those missing this data in the NIH All of Us database. PURPOSE: To characterize missing data among glaucoma patients within All of Us. PATIENTS AND METHODS: We used diagnosis codes to define cohorts of primary glaucoma patients with and without severity staging specified. Descriptive analyses were conducted by presence of disease severity stage. Analysis of missing data was conducted using a set intersection plot and Little's Test of Missing Completely at Random. T-tests were performed to evaluate differences. RESULTS: Of 2982 participants, 1714 (57%) did not have glaucoma severity stage specified, and 11 of 23 analyzed variables had missing data. Little's Test indicated data was not missing completely at random (P<0.001). Significant differences existed between the two cohorts with respect to age, age of first glaucoma diagnosis, gender, ethnicity, education, income, insurance, history of glaucoma surgery and medication use, and answers regarding ability to afford eyeglasses and having seen an eye care provider in the last 12 months (all P values≤0.01). CONCLUSION: There were significant differences between glaucoma participants with glaucoma severity stage specified versus those with unstaged disease across multiple socioeconomic characteristics and self-reported barriers to care. Glaucoma severity staging data was not missing completely at random. The unstaged cohort included higher rates of multiple underrepresented communities, which may potentially contribute to bias in ophthalmology research as participants from vulnerable populations may be disproportionately excluded from electronic health records or claims data studies where diagnosis codes with severity/staging levels are used to examine risk factors for disease, progression, and treatment efficacy.

Long-Term Systemic Use of Calcium Channel Blockers and Incidence of Primary Open-Angle Glaucoma.

PURPOSE: To evaluate the association between the systemic use of calcium channel blockers (CCBs) and primary open-angle glaucoma (POAG) using a diverse nationwide dataset. DESIGN: Retrospective cohort study. SUBJECTS: 213 424 individuals aged 40 years and older in the National Institutes of Health All of Us dataset, notable for its demographic, geographic, and medical diversity and inclusion of historically underrepresented populations. Patients with a diagnosis of POAG prior to use of any kind of antihypertensive medication were excluded. METHODS: Bivariate and multivariable regression analyses were performed to evaluate associations between CCB use and POAG. Calcium channel blocker use was further divided into exposure to dihydropyridine CCBs and nondihydropyridine CCBs, and subgroup analyses were performed using chi-square and Fisher tests. MAIN OUTCOME MEASURES: Diagnosis of POAG. RESULTS: Within our cohort, 2772 participants (1.3%) acquired a diagnosis of POAG, while 210 652 (98.7%) did not. Among patients who developed POAG, the mean age was 73.3 years, 52.5% were female, and 48.2% identified as White. Among patients with POAG, 32.6% used 1 or more CCB, 28.2% used a dihydropyridine CCB, and 2.2% used a nondihydropyridine CCB. In bivariate analysis, use of any CCBs was associated with an increased risk of POAG (odds ratio [OR]: 1.29, 95% confidence interval [CI]: 1.27-1.31, P < 0.001). In multivariable analysis adjusting for age, gender, race, ethnicity, and comorbidities such as diabetes, hyperlipidemia, and hypertension, use of any CCBs remained associated with an increased risk of developing POAG (OR: 1.52, 95% CI: 1.33-1.74, P < 0.001). When stratified by type of CCB, the use of dihydropyridine CCBs (OR: 1.31, 95% CI: 1.14-1.50, P < 0.001) was associated with increased POAG risk. CONCLUSIONS: Use of dihydropyridine CCBs was associated with a significantly higher risk of developing POAG, both before and while adjusting for demographic factors and comorbid medical conditions. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Evaluation of bias and gender/racial concordance based on sentiment analysis of narrative evaluations of clinical clerkships using natural language processing.

There is increasing interest in understanding potential bias in medical education. We used natural language processing (NLP) to evaluate potential bias in clinical clerkship evaluations. Data from medical evaluations and administrative databases for medical students enrolled in third-year clinical clerkship rotations across two academic years. We collected demographic information of students and faculty evaluators to determine gender/racial concordance (i.e., whether the student and faculty identified with the same demographic). We used a multinomial log-linear model for final clerkship grades, using predictors such as numerical evaluation scores, gender/racial concordance, and sentiment scores of narrative evaluations using the SentimentIntensityAnalyzer tool in Python. 2037 evaluations from 198 students were analyzed. Statistical significance was defined as P < 0.05. Sentiment scores for evaluations did not vary significantly by student gender, race, or ethnicity (P = 0.88, 0.64, and 0.06, respectively). Word choices were similar across faculty and student demographic groups. Modeling showed narrative evaluation sentiment scores were not predictive of an honors grade (odds ratio [OR] 1.23, P = 0.58). Numerical evaluation average (OR 1.45, P < 0.001) and gender concordance between faculty and student (OR 1.32, P = 0.049) were significant predictors of receiving honors. The lack of disparities in narrative text in our study contrasts with prior findings from other institutions. Ongoing efforts include comparative analyses with other institutions to understand what institutional factors may contribute to bias. NLP enables a systematic approach for investigating bias. The insights gained from the lack of association between word choices, sentiment scores, and final grades show potential opportunities to improve feedback processes for students.

Disparities in Eye Care Utilization Among Refugee and Migrant Populations.

Purpose: In this cross-sectional study, we examined refugee/migrant participants' health and eye care utilization compared to controls in San Diego County. Methods: Data were collected from electronic health records (EHRs) at UCSD Health-affiliated medical centers. Through a manual review of EHRs, eligibility criteria to identify a cohort were developed. A total of 64 refugee/migrant participants and 95 control participants matched based on country of origin, age, and sex were included in the analysis. Demographic characteristics, insurance type, and vision/eye care utilization were compared between the two groups. Results: A greater proportion of refugee/migrant participants were more likely to be enrolled in government-sponsored insurance programs, predominantly Medicaid when compared to controls (55% vs. 24%, P = < 0.01). When adjusting for age, history of ophthalmic procedure, and surgery, refugee status was associated with fewer encounters with ophthalmologists in a multivariable linear regression model (coefficient = -1.66 [95% confidence interval [CI] = -2.89 to -0.44], P = 0.009). Conclusions: This study highlights disparities in eye care utilization for refugee/migrant populations. When compared to controls, a larger proportion of refugees/migrants had government-funded insurance, and refugee status was associated with fewer encounters with ophthalmologists. These findings underscore the need for further research on this population to better understand potential healthcare barriers these individuals may encounter. Translational Relevance: This analysis of EHR data illustrates disparities in eye care experienced by refugees/migrants, highlighting potential gaps in care in a vulnerable population.

Proactive Decision Support for Glaucoma Treatment: Predicting Surgical Interventions with Clinically Available Data.

A longitudinal ophthalmic dataset was used to investigate multi-modal machine learning (ML) models incorporating patient demographics and history, clinical measurements, optical coherence tomography (OCT), and visual field (VF) testing in predicting glaucoma surgical interventions. The cohort included 369 patients who underwent glaucoma surgery and 592 patients who did not undergo surgery. The data types used for prediction included patient demographics, history of systemic conditions, medication history, ophthalmic measurements, 24-2 VF results, and thickness measurements from OCT imaging. The ML models were trained to predict surgical interventions and evaluated on independent data collected at a separate study site. The models were evaluated based on their ability to predict surgeries at varying lengths of time prior to surgical intervention. The highest performing predictions achieved an AUC of 0.93, 0.92, and 0.93 in predicting surgical intervention at 1 year, 2 years, and 3 years, respectively. The models were also able to achieve high sensitivity (0.89, 0.77, 0.86 at 1, 2, and 3 years, respectively) and specificity (0.85, 0.90, and 0.91 at 1, 2, and 3 years, respectively) at an 0.80 level of precision. The multi-modal models trained on a combination of data types predicted surgical interventions with high accuracy up to three years prior to surgery and could provide an important tool to predict the need for glaucoma intervention.

Gender Disparities in Depression, Stress, and Social Support Among Glaucoma Patients.

Purpose: To understand differences in measures of depression, stress, and social support by gender among those diagnosed with glaucoma. Methods: We obtained a cohort of glaucoma patients (any type) ages 18 years and over who answered the COVID-19 Participant Experience (COPE) survey of the NIH All of Us Research Program. We analyzed several measures of depression, stress, and social support by gender. Logistic regression was used to evaluate the association among reported stress associated with social distancing, depression (using Patient Health Questionnaire-9 [PHQ-9] scores), and measures of social support by self-reported gender, with men as the reference group. Multivariable models were adjusted for age, race and ethnicity, health insurance status, education, and income. Results: Of 3633 glaucoma patients, 56.8% were women. Many patients had a PHQ-9 score > 4 (33.3%), indicating mild, moderate, or severe depression. In multivariable models, women were significantly more likely to report a PHQ-9 score > 4 (odds ratio [OR] = 1.40; 95% confidence interval [CI], 1.20-1.62; P < 0.001) and some or a lot of stress (OR = 1.34; 95% CI, 1.14-1.57; P < 0.001) compared with men. Further, women were significantly less likely to report having help all or most of the time if they needed someone to prepare meals (OR = 0.78; 95% CI, 0.67-0.92; P = 0.002) or perform daily chores (OR = 0.79; 95% CI, 0.67-0.91; P = 0.003) than men. Conclusions: Women with glaucoma were more likely to experience depression and stress and were less likely to have social support on some measures than men. Translational Relevance: The disproportionate burden of psychosocial factors among women may complicate glaucoma management.